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I don't see the problem in sampling only professional women when the hypothesis is whether professional women are rated differently than professional men. It wo

by blutoot 12y ago

I don't see the problem in sampling only professional women when the hypothesis is whether professional women are rated differently than professional men. It would be problematic if this hypothesis was tested with a sample from the overall female population. Non-professional women don't have reviews to be useful to the study. What you described is an explanation for WHY they are rated different. It doesn't discredit the study methodology.

spindritf 12y ago
hypothesis is whether professional women are rated differently than professional men The point is that you can't know whether they're rated differently (by different criteria) or actually different.
- lclarkmichalek 12y ago
  I think one of the premises you have to accept in this article is that the actual difference between women and men is less than (looking at the "has negative feedback" vs "only constructive criticism" chart) 72%. I don't find that a hard premise to accept
  - NotAtWork 12y ago
    I don't actually want to be arguing against this study, but without knowing how those two fields are quantified, those numbers are completely meaningless and referring to fuzzy numbers like that is one of the hallmark ways to lie with statistics. There are also lots of uncontrolled variables, such as the average woman submitting 1.4 reviews, while the average guy submitted only 1.3 reviews. This means the totals would obviously be off, even if everything were symmetric per capita, a fact not mentioned when the numbers are displayed. Further, it's likely that the discrepancy in the number of reviews per capita submitted is a sign of some underlying sampling bias, which needs to be accounted for before we can really talk about the distribution of feedback. I think this is a serious issue that needs addressing, but that's exactly why I feel it's important to object to bad math.
NotAtWork 12y ago
This article lacked the power to tell if the effect (ie, different language in reviews) was correlated to their gender or because of a selection bias in the people studied, eg because of a possible difference in the distribution of personality types in the field from each gender. It's possible that there's a hiring bias, and reviews are being done fairly, but show biased results when naively studied because of the underlying bias. This is what makes social statistics hard, and there are many faults in studies involving gender that fail to account for possible confounding effects in the data.
cperciva 12y ago
What you described is an explanation for WHY they are rated different. It doesn't discredit the study methodology. Right, and I'm not objecting to the conclusion "women in tech are described as being more aggressive than men in tech". What I'm objecting to is the logical jump from there to assuming that the difference is due to a bias in the "described" part rather than a bias in which people enter the tech field.

by blutoot 12y ago

spindritf 12y ago

hypothesis is whether professional women are rated differently than professional men The point is that you can't know whether they're rated differently (by different criteria) or actually different.

lclarkmichalek 12y ago
I think one of the premises you have to accept in this article is that the actual difference between women and men is less than (looking at the "has negative feedback" vs "only constructive criticism" chart) 72%. I don't find that a hard premise to accept
- NotAtWork 12y ago
  I don't actually want to be arguing against this study, but without knowing how those two fields are quantified, those numbers are completely meaningless and referring to fuzzy numbers like that is one of the hallmark ways to lie with statistics. There are also lots of uncontrolled variables, such as the average woman submitting 1.4 reviews, while the average guy submitted only 1.3 reviews. This means the totals would obviously be off, even if everything were symmetric per capita, a fact not mentioned when the numbers are displayed. Further, it's likely that the discrepancy in the number of reviews per capita submitted is a sign of some underlying sampling bias, which needs to be accounted for before we can really talk about the distribution of feedback. I think this is a serious issue that needs addressing, but that's exactly why I feel it's important to object to bad math.

NotAtWork 12y ago

This article lacked the power to tell if the effect (ie, different language in reviews) was correlated to their gender or because of a selection bias in the people studied, eg because of a possible difference in the distribution of personality types in the field from each gender. It's possible that there's a hiring bias, and reviews are being done fairly, but show biased results when naively studied because of the underlying bias. This is what makes social statistics hard, and there are many faults in studies involving gender that fail to account for possible confounding effects in the data.

cperciva 12y ago

What you described is an explanation for WHY they are rated different. It doesn't discredit the study methodology. Right, and I'm not objecting to the conclusion "women in tech are described as being more aggressive than men in tech". What I'm objecting to is the logical jump from there to assuming that the difference is due to a bias in the "described" part rather than a bias in which people enter the tech field.